Understanding Fine-tuning
Why Fine-tune LLMs?
Pre-trained models like Llama, Mistral, and Qwen are incredibly capable out of the box. So why would you spend time and compute to fine-tune them?
The Limitations of Pre-trained Models
Pre-trained LLMs are generalists. They know a lot about everything, but they may not know enough about your specific domain:
| Challenge | Example |
|---|---|
| Domain vocabulary | Medical, legal, or industry-specific terminology |
| Company knowledge | Internal processes, product names, policies |
| Task specialization | Specific output formats, coding styles |
| Tone and style | Brand voice, formal vs casual communication |
When Fine-tuning Makes Sense
Fine-tuning is the right choice when you need:
1. Domain Expertise
Train a model on your proprietary documentation, research papers, or specialized knowledge base.
Pre-trained: "A synapse is a junction between neurons."
Fine-tuned (Medical): "A synapse is the specialized junction where
neurotransmitter release occurs via calcium-dependent exocytosis,
with typical synaptic delay of 0.5-1ms..."
2. Consistent Output Format
Ensure the model always responds in a specific structure—JSON, XML, or your custom format.
3. Task Specialization
Create models optimized for specific tasks like:
- Code generation in your stack
- Customer support for your product
- Legal document analysis
- Scientific paper summarization
4. Cost Reduction
A smaller, fine-tuned 7B model can outperform a general-purpose 70B model on your specific task—at 1/10th the inference cost.
The ROI of Fine-tuning
| Metric | Before Fine-tuning | After Fine-tuning |
|---|---|---|
| Task accuracy | 65-75% | 90-95% |
| Inference cost | $10/1M tokens | $1/1M tokens (smaller model) |
| Response consistency | Variable | Highly consistent |
| Domain knowledge | Generic | Specialized |
What You'll Learn in This Course
- Choose the right fine-tuning method for your use case
- Prepare high-quality training datasets
- Fine-tune models using LoRA and QLoRA with minimal hardware
- Use Unsloth for 2x faster training with 70% less VRAM
- Align models using DPO (Direct Preference Optimization)
- Deploy your fine-tuned models to Ollama
Let's start by understanding the different types of fine-tuning available. :::